In conventional computers, complementary metal-oxide-semiconductor (CMOS) transistor technology and Von Neumann architectures are used to implement the computing elements. However, these computers, as commonly implemented, can have disadvantages. Notably, the power requirements are often higher for these systems. For some Big Data applications, the conventional computing paradigm can require over an order of magnitude greater power usage versus competing paradigms, such as neuromorphic systems.
In biological systems, the point of contact between an axon of a neuron and a dendrite of a second neuron is referred to as a synapse. It is widely viewed that the synapse plays an essential role in the formation of memory. As a neurotransmitter activates a receptor across a synaptic cleft, the connection between the two neurons is strengthened when both neurons are active at the same time, as a result of the receptor's signaling mechanisms. The strength of two connected neural pathways is thought to result in the storage of information, resulting in memory. This process of synaptic strengthening is known as long-term potentiation. That is, the synaptic conductance changes with time as a function of the relative spike times of pre-synaptic and post-synaptic neurons, as per spike-timing dependent plasticity (STDP). The spike-timing dependent plasticity increases the conductance of a synapse if its post-synaptic neuron fires after its pre-synaptic neuron fires, and decreases the conductance of a synapse if the order of the two firings is reversed.
Neuromorphic or artificial neural network systems are computational systems that function in a manner analogous to that of biological neural systems. Neuromorphic systems generally do not follow the traditional model of manipulating binary data. Instead, neuromorphic systems have connections between processing elements that attempt to mirror the neurons of a biological neural system. As such, neuromorphic systems may include various electronic circuit elements that are modeled on neurons.
Neuromorphic computers may allow machines the ability to perform complex functions by mimicking the brain. The natural ability of the brain to perform a high number of complex functions in parallel that have significantly better capabilities than many computers along several metrics. These future neuromorphic processors may have a major impact of computing, particularly in terms of efficiency. Application areas, such as database manipulation and searches, image processing for radar application, simultaneous localization and mapping, and medical imaging processing, can see substantial benefits from the technology. As data sets become larger, there is a need for a fundamental change in how computers are architected. Neuromorphic architectures can scale to these data sets, while providing better performance in terms of size and power requirements.
Previous neuromorphic computing implementations have demonstrated the feasibility of mimicking brain functionality. However, current implementations of neuromorphic computing elements have shortcomings in their overall effectiveness. Some previous neuromorphic circuits have focused on using inhibitory links. These architectures may use the output of a neuron to disable other neurons. For example, each neuron may inhibit the integration of all the other neurons during a time interval after a spike. In such a winner-take-all configuration, only the neuron with the highest activation stays active while all other neurons shut down. However, these configurations can have poor performance in learning multiple correlations compared to other configurations. Also, these configurations have limited reliability and insight on the features of the input data. There still remains the potential for substantial improvement through novel circuit architectures.